scholarly journals Automated Analysis Using a Bayesian Functional Mixed-Effects Model With Gaussian Process Responses for Wavelet Spectra of Spatiotemporal Colonic Manometry Signals

2021 ◽  
Vol 11 ◽  
Author(s):  
Lukasz Wiklendt ◽  
Marcello Costa ◽  
Mark S. Scott ◽  
Simon J. H. Brookes ◽  
Phil G. Dinning
Keyword(s):  
Spectral Analysis ◽  
Wavelet Transform ◽  
Statistical Inference ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Model Parameters ◽  
Slow Transit Constipation ◽  
Colonic Manometry ◽  
Automated Method ◽  
Manual Analysis

Manual analysis of human high-resolution colonic manometry data is time consuming, non-standardized and subject to laboratory bias. In this article we present a technique for spectral analysis and statistical inference of quasiperiodic spatiotemporal signals recorded during colonic manometry procedures. Spectral analysis is achieved by computing the continuous wavelet transform and cross-wavelet transform of these signals. Statistical inference is achieved by modeling the resulting time-averaged amplitudes in the frequency and frequency-phase domains as Gaussian processes over a regular grid, under the influence of categorical and numerical predictors specified by the experimental design as a functional mixed-effects model. Parameters of the model are inferred with Hamiltonian Monte Carlo. Using this method, we re-analyzed our previously published colonic manometry data, comparing healthy controls and patients with slow transit constipation. The output from our automated method, supports and adds to our previous manual analysis. To obtain these results took less than two days. In comparison the manual analysis took 5 weeks. The proposed mixed-effects model approach described here can also be used to gain an appreciation of cyclical activity in individual subjects during control periods and in response to any form of intervention.

Nitrogen mineralisation in sugarcane soils in Queensland, Australia: I. evaluation of soil tests for predicting nitrogen mineralisation

Soil Research ◽  
2019 ◽  
Vol 57 (7) ◽  
pp. 738 ◽  
Author(s):  
D. E. Allen ◽  
P. M. Bloesch ◽  
T. G. Orton ◽  
B. L. Schroeder ◽  
D. M. Skocaj ◽  
...  
Keyword(s):  
Soil Properties ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Co2 Production ◽  
Organic N ◽  
Model Parameters ◽  
Nitrogen Mineralisation ◽  
Short Term ◽  
Available N

We explored soil properties as indices of mineralisable nitrogen (N) in sugarcane soils and whether we could increase the accuracy of predicting N mineralisation during laboratory incubations. Utilising historical data in combination with samples collected during 2016, we: (i) measured mineralised N over the course of short-term (14 days) and long-term (301 days) laboratory incubations; (ii) compared models representing mineralisation; then (iii) related model parameters to measured soil properties. We found measures representing the labile organic N pool (Hydrolysable NaOH organic N; amino sugar Illinois soil N test) best related to short-term mineralised N (R2 of 0.50–0.57, P < 0.001), while measures of CO2 production (3, 7, 10 and 14 days) best related to longer-term mineralised N (R2 of 0.75–0.84, P < 0.001). Indices were brought together to model the active and slow pools of a two-pool mineralisation model in the statistical framework of a mixed-effects model. Of the models that relied on measurement of one soil property, cumulative CO2 production (7 days) performed the best when considering all soil types; in a cross-validation test, this model gave an external R2 of 0.77 for prediction of the 301-day mineralised N. Since the mixed-effects model accounts for the various sources of uncertainty, we suggest this approach as a framework for prediction of in-field available N, with further measurement of long-term mineralised N in other soils to strengthen predictive certainty of these soil indices.

scholarly journals Modelling the Tree Height, Crown Base Height, and Effective Crown Height of Pinus koraiensis Plantations Based on Knot Analysis

Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1778
Author(s):  
Wancai Zhu ◽  
Zhaogang Liu ◽  
Weiwei Jia ◽  
Dandan Li
Keyword(s):  
Tree Height ◽  
Information Criterion ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Pinus Koraiensis ◽  
Tree Level ◽  
Model Parameters ◽  
Crown Height ◽  
Crown Base Height ◽  
Pruning Intensity

Taking 1735 Pinus koraiensis knots in Mengjiagang Forest Farm plantations in Jiamusi City, Heilongjiang Province as the research object, a dynamic tree height, effective crown height, and crown base height growth model was developed using 349 screened knots. The Richards equation was selected as the basic model to develop a crown base height and effective crown height nonlinear mixed-effects model considering random tree-level effects. Model parameters were estimated with the non-liner mixed effect model (NLMIXED) Statistical Analysis System (SAS) module. The akaike information criterion (AIC), bayesian information criterion (BIC), −2 Log likelihood (−2LL), adjusted coefficient (Ra2), root mean square error (RMSE), and residual squared sum (RSS) values were used for the optimal model selection and performance evaluation. When tested with independent sample data, the mixed-effects model tree effects-considering outperformed the traditional model regarding their goodness of fit and validation; the two-parameter mixed-effects model outperformed the one-parameter model. Pinus koraiensis pruning times and intensities were calculated using the developed model. The difference between the effective crown and crown base heights was 1.01 m at the 15th year; thus, artificial pruning could occur. Initial pruning was performed with a 1.01 m intensity in the 15th year. Five pruning were required throughout the young forest period; the average pruning intensity was 1.46 m. The pruning interval did not differ extensively in the half-mature forest period, while the intensity decreased significantly. The final pruning intensity was only 0.34 m.

A note on estimating single-class piecewise mixed-effects models with unknown change points

2018 ◽  
Vol 42 (5) ◽  
pp. 518-524 ◽  
Author(s):  
Nidhi Kohli ◽  
Yadira Peralta ◽  
Cengiz Zopluoglu ◽  
Mark L. Davison
Keyword(s):  
Bayesian Estimation ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Mixed Effects Models ◽  
Change Points ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Residual Variance ◽  
Monte Carlo Simulation Study ◽  
Ml Estimation

Piecewise mixed-effects models are useful for analyzing longitudinal educational and psychological data sets to model segmented change over time. These models offer an attractive alternative to commonly used quadratic and higher-order polynomial models because the coefficients obtained from fitting the model have meaningful substantive interpretation. The current study thus focuses on the estimation of piecewise mixed-effects model with unknown random change points using maximum likelihood (ML) as described in Du Toit and Cudeck (2009). Previous simulation work (Wang & McArdle, 2008) showed that Bayesian estimation produced reliable parameter estimates for the piecewise model in comparison to frequentist procedures (i.e., first-order Taylor expansion and the adaptive Gaussian quadrature) across all simulation conditions. In the current article a small Monte Carlo simulation study was conducted to assess the performance of the ML approach, a frequentist procedure, and the Bayesian approach for fitting linear–linear piecewise mixed-effects model. The obtained findings show that ML estimation approach produces reliable and accurate estimates under the conditions of small residual variance of the observed variables, and that the size of the residual variance had the most impact on the quality of model parameter estimates. Second, neither ML nor Bayesian estimation procedures performed well under all manipulated conditions with respect to the accuracy and precision of the estimated model parameters.

scholarly journals CALIBRATION OF MATERIAL MODEL PARAMETERS USING MIXED-EFFECTS MODEL

2021 ◽  
Author(s):  
Clément Laboulfie ◽  
Matthieu Balesdent ◽  
Loïc Brevault ◽  
Sébastien Da Veiga ◽  
François-Xavier Irisarri ◽  
...  
Keyword(s):  
Material Model ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Model Parameters

scholarly journals A flexible nonlinear mixed effects model for HIV viral load rebound after treatment interruption

2020 ◽  
Vol 39 (15) ◽  
pp. 2051-2066 ◽  
Author(s):  
Rui Wang ◽  
Ante Bing ◽  
Cathy Wang ◽  
Yuchen Hu ◽  
Ronald J. Bosch ◽  
...  
Keyword(s):  
Viral Load ◽  
Treatment Interruption ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Nonlinear Mixed Effects ◽  
Hiv Viral Load ◽  
Nonlinear Mixed Effects Model ◽  
Viral Load Rebound

scholarly journals The state of Spanish /s/ variation in Concepción, Chile: Linguistic and social trends

2020 ◽  
Vol 6 (1) ◽  
pp. 132-153
Author(s):  
Brandon M. A. Rogers
Keyword(s):  
Social Factors ◽  
Central City ◽  
Mixed Effects ◽  
The State ◽  
Mixed Effects Model ◽  
Proportional Odds ◽  
Random Factor ◽  
Social Trends ◽  
Southern Central ◽  
Social Demographics

AbstractThe current study examines /s/ variation in the southern-central city of Concepción, Chile and its relation to a variety of linguistic and social factors. A proportional-odds mixed effects model, with the random factor of “speaker”, was used to treat the categorically coded data on a continuum of acoustical variation ([s] > [h] > ∅). The results presented show that contrary to the previous assertions, heavy sibilant reduction, especially elision, in Concepción, Chile is the rule, rather than the exception, to the extent that it is no longer a marker of certain social demographics as has been reported previously. Furthermore, based on the trends reported, it is likely that this has been the case for several decades. Finally, the overall observed trends are indicative that the rates of /s/ elision will continue to increase across social demographics and different phonetic and phonological contexts in Concepción, Chile.

Estimating local arterial stiffness using mixed-effects model-based residuals: a novel approach

2021 ◽  
Author(s):  
Avinash Chandran ◽  
Derek W. Brown ◽  
Gabriel H. Zieff ◽  
Zachary Y. Kerr ◽  
Daniel Credeur ◽  
...  
Keyword(s):  
Arterial Stiffness ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Model Based ◽  
Novel Approach
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